Enabling Virtual Knowledge Networks for Human Rights Monitoring for People with Disabilities
Bibliographic record
Abstract
Holistic disability rights monitoring is an imperative approach to permit translation of rights on paper into rights in reality for people with disabilities. However, evidence-based knowledge produced through such a holistic monitoring approach has to be accessible to a broad range of stakeholders, e.g., groups such as: researchers, representatives of disability community, people with disabilities, media, policy makers, and the general public. Besides, the collected evidence should contribute to building capacity within disability community around human rights questions. This article explains the design process of a Virtual Knowledge Network (VKN) as an operational tool to support mobilization and dissemination of evidence-based knowledge produced by the Disability Rights Promotion International Canada (DRPI-Canada) project. This VKN is embedded in the more general framework of DRPI, grounded in a human rights approach to disability that acknowledges the importance of creating knowledgeable communities in order to make the disability rights monitoring efforts sustainable.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".